Unable to predict in SVM OpenCV 3.0 - c++

Question

I have been able to train my SVM. The program can run until it comes to prediction. I'm getting an error for SVM prediction with the testing images.
What have I missed in the code? Can anybody help me?
OpenCV Error: Assertion failed (samples.cols == var_count && samples.type() == CV_32F) in cv::ml::SVMImpl::predict, file C:\buildslave64\win64_amdocl\master_PackSlave-win64-vc14-shared\opencv\modules\ml\src\svm.cpp, line 1930
My prediction code is found below:
#include <opencv2/core.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>
#include <iostream>
#include <fstream>
#include<string.h>
using namespace std;
using namespace cv;
using namespace cv::ml;
int main(int, char**)
{
HOGDescriptor hog(cv::Size(64, 128), cv::Size(16, 16), cv::Size(8, 8), cv::Size(8, 8), 9, 1, -1, 0, 0.2, true, HOGDescriptor::DEFAULT_NLEVELS);
vector<cv::Point> locations;
std::vector<float> extractedFeature;
vector<vector< float>> features;
vector<Mat> testingImages;
vector<int> testingLabels;
int numFiles = 11; //no. of rows in matrix
int img_area = 320 * 240; //no. of columns - area of image 76800
FileStorage myfile("features.xml", FileStorage::READ);
const char* path = "C:/Testing Set/Extracted_Frames/image";
//set up labels for each training image
float label = 1.0; //positive image +1
Mat testingMat(img_area, numFiles, CV_32FC1);// 1D training matrix
cout << testingMat.rows << endl;
cout << testingMat.cols << endl;
Mat res; // output
//set up labels for each training image
Mat labels(testingMat.rows, 1, CV_32SC1, label); //flatten 1D label matrix
Ptr<ml::SVM> svm = Algorithm::load<ml::SVM>("test.xml");
std::cout << "Model Loaded" << std::endl;
for (int i = 0; i < labels.rows; i++) {
labels.at<int>(i, 0) = labels.at<int>(i, 0);
}
for (int file_num = 0; file_num < numFiles; file_num++)
{
stringstream ss(stringstream::in | stringstream::out);
ss << path << file_num << ".jpg";
cout << "read path = " << ss.str() << endl;
myfile["Descriptors" + ss.str()] >> extractedFeature;
Mat img = imread(ss.str());
int ii = 0; // Current column in training_mat
for (int i = 0; i < img.rows; i++) {
for (int j = 0; j < img.cols; j++) {
testingMat.at<float>(ii++, file_num) = img.at<uchar>(i, j);
Mat sampleMat = (Mat_<float>(1, 2) << i, j);
float response = svm->predict(sampleMat);//error here
}
}
features.push_back(extractedFeature);
testingImages.push_back(img);
testingLabels.push_back(1);
testingLabels.push_back(file_num);
myfile.release();
}
labels.at<int>(1, 0) = -1;
}